Hazelcast

DLT and Hazelcast have partnered to deliver high-performance software systems that address your enterprise's large-scale data processing requirements. We help with a wide variety of use cases that operate on large volumes of time-sensitive data, including:

• Stream and batch processing processing
• Low-latency JSON and object data store for applications
• Extract-transform-load (ETL)
• Digital integration hub / materialized views 
• Database acceleration
• Batch processing
• Mainframe integration
• Cache-as-a-service

Our flexible data platform simplifies the deployment of modern architectural patterns such as:

• Machine learning inference and AI workloads
• Event-driven microservices
• Multi-cloud deployments
• Real-time stream processing
• Digital integration hubs

Hazelcast Platform is the foundational architecture providing core capabilities for AI and critical applications. With a revolutionary approach, Hazelcast Platform combines distributed compute, in-memory data storage, integrated stream processing, and vector search to simplify application development, deployment, and maintenance. The platform is relied upon by many Global 2000 enterprises in financial services, e-commerce, logistics, and other industries vital to individuals and businesses. Hazelcast is headquartered in San Mateo, CA, with offices across the globe. To learn more about Hazelcast, visit hazelcast.com.

The Hazelcast In-Memory Computing Platform ("Hazelcast Platform") is an easy-to-deploy, API-based software package that accelerates large-scale, data-intensive infrastructures in the cloud, at the edge, or on-premises. It is available as open-source software, licensed software with advanced features, and as a fully managed cloud service on all the major cloud providers. Hazelcast is used to augment any system that requires higher throughput and/or lower latency data processing.

The platform consists of two components ("Hazelcast IMDG" and "Hazelcast Jet") which enable high performance via three main capabilities:

In-memory data storage. Hazelcast offers an in-memory data grid (IMDG) as part of its platform ("Hazelcast IMDG"), which can be used as a high-speed data store that leverages SQL or the data object-based API. It coordinates the access of data objects across a cluster of networked computers, thus pooling together the RAM from the computers into one large virtual block of RAM. It has many built-in optimization strategies that help reduce network accesses when retrieving data to deliver the highest data access speeds.

Distributed stream processing. Hazelcast offers a distributed stream processing engine ("Hazelcast Jet") that is designed for high-speed processing of large volumes of data. It coordinates the processing effort across multiple networked machines as a cluster by assigning subtasks to each of the computers. It is typically used for taking action on an unlimited, incoming feed of data (i.e., a “data stream”). It is packaged as a software library with an application programming interface (API) that lets software engineers transform, enrich, aggregate, filter, and analyze data at high speeds.

Distributed batch processing. Hazelcast provides distributed batch processing capabilities via the API mentioned above, that breaks down large-scale workloads into smaller tasks that are run in parallel to take advantage of all the hardware resources in the cluster. Both Hazelcast Jet and Hazelcast IMDG have provisions to deliver software code to each node in the cluster, which simplifies the development of distributed applications. Hazelcast uses data-locality strategies so that each task operates on the data on the same node, thus minimizing data movement across the network to significantly reduce latency.

Hazelcast provides system acceleration to a wide variety of solutions, running on-premises, at the edge, or in the cloud (including multi and hybrid clouds). Hazelcast enables:

Cache-as-a-service. To accelerate access to commonly used data, Hazelcast enables a large common data repository with in-memory speeds. Instead of building a separate cache per application, a centrally managed system can provide an API-based caching service. Hazelcast pools RAM from multiple hardware servers in a cluster, enabling a large in-memory data cache that can be leveraged by multiple applications and teams. Data can be shared as needed, or isolated/secured by using built-in role-based access controls.

Common operational picture. Static reference data and dynamically changing mission data can be combined and stored in-memory across a cluster of hardware servers to provide a high-speed analytics repository. Continuous querying can enable real-time decision support. Built-in capabilities for continuity and security ensure the system is reliable and protected.

Database acceleration. Data-intensive applications are often limited in performance by I/O bottlenecks. Since many applications often have repetitive data access patterns, using Hazelcast as a data layer between applications and your disk-based databases will dramatically improve access times. Hazelcast supports write-through capabilities so you are not limited to read-only data, but can also use Hazelcast to update your database to keep the data current.

Internet of Things and edge analytics. With its stream processing capabilities and a large portfolio of pre-built connectors to popular data platforms, Hazelcast can filter, aggregate, transform, and analyze the high-speed data coming from numerous devices. Since it is a lightweight and portable software package, it can be used in resource-constrained, small footprint hardware that is typical in edge deployments.

Operational data store. Hazelcast not only provides in-memory storage but also distributed computing, to enable parallelized operations on the in-memory data set. Applications are submitted as “jobs” to operate on the local data where the application instances are running, to minimize slow network accesses. Optimizations like “near-cache” copy data from other servers to the local server for situations where remote data is needed, and eliminates the network latency associated with future accesses to that same remote data.

Operations optimization. Hazelcast is ideal for capturing operational data and running analysis to streamline operations. This is often used in conjunction with machine learning frameworks to create algorithms/models that identify ways to reduce workflow bottlenecks and to create greater operational efficiency. Hazelcast ensures the execution of those machine learning models are fast and reliable.

Programmatic extract-transform-load (ETL). Data is often transformed and enriched in ways that cannot be handled by GUI-based tools. Hazelcast provides pre-built data connectors to a wide set of data platforms that act as the systems of record. The Hazelcast API lets you store data as objects and quickly transform/enrich data for further analysis. That data can be queried within the Hazelcast in-memory store, or it can be delivered to another data platform for analytics.

Risk/threat assessment. Risk and threat assessment typically requires fast analysis of real-time data. The combination of fast data ingestion and in-memory processing in Hazelcast enables a broad range of capability to analyze data and understand relationships that represent risks that need immediate responses.

Simulations. Large-scale simulations require billions or trillions of calculations, far beyond what a single computer can handle. With Hazelcast, the calculations can be distributed across the cluster to take advantage of all the available processing power to calculate the output faster. More servers can be incrementally added to the cluster to add processing power to handle larger simulations, or to deliver results faster. Continuity capabilities ensure that should any hardware failure occur during the simulation, the remaining servers can continue processing with no interruption.